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ViLA: Efficient Video-Language Alignment for Video Question Answering
In this work, we propose an efficient Video-Language Alignment (ViLA) network.
Ranked #1 on
Video Question Answering
on STAR Benchmark
MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation
Furthermore, via experiments on the NYUv2 and IBims-1 datasets, we demonstrate that these enhanced representations translate to performance improvements in both the in-distribution and out-of-distribution settings.
Ranked #11 on
Monocular Depth Estimation
on NYU-Depth V2
HR-NeuS: Recovering High-Frequency Surface Geometry via Neural Implicit Surfaces
Recent advances in neural implicit surfaces for multi-view 3D reconstruction primarily focus on improving large-scale surface reconstruction accuracy, but often produce over-smoothed geometries that lack fine surface details.
RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View
In addition to using learnt robust features, our approach learns an additional ranking function to estimate the final layout instead of using optimization.
